Integrating the lot-sizing and sequencing decisions for scheduling a capacitated flow line
Computers and Industrial Engineering
A genetic algorithm for integrating lot-sizing and sequencing in scheduling a capacitated flow line
Computers and Industrial Engineering
An FPTAS for a single-item capacitated economic lot-sizing problem with monotone cost structure
Mathematical Programming: Series A and B
Lot sizing and sequencing optimisation at an animal-feed plant
Computers and Industrial Engineering
Batching work and rework processes with limited deterioration of reworkables
Computers and Operations Research
Hybrid rounding techniques for knapsack problems
Discrete Applied Mathematics - Special issue: Efficient algorithms
Lot-sizing and scheduling problem with earliness tardiness and setup penalties
Computers and Industrial Engineering
Supply Chain Engineering
Computers and Industrial Engineering
Optimal production lot sizing with rework, scrap rate, and service level constraint
Mathematical and Computer Modelling: An International Journal
Algorithms for the bounded set-up knapsack problem
Discrete Optimization
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A single-machine multi-product lot-sizing and sequencing problem is studied. In this problem, items of n different products are manufactured in lots. Demands for products as well as per item processing times are known. There are losses of productivity because of non perfect production. There is also a sequence dependent set-up time between lots of different products. Machine yields and product lead times are assumed to be known deterministic functions. The objective is to minimize the cost of the demand dissatisfaction provided that the total processing time does not exceed a given time limit. We propose two integer linear programming (ILP) models for the NP-hard ''fraction defective'' case of this problem and compare effectiveness of their ILOG CPLEX realizations with a dynamic programming algorithm in a computer experiment. We also show how an earlier developed fully polynomial time approximation scheme (FPTAS) and one of the ILP models can be extended for a more complex case.